Investigating flowering time in wheat under controlled environment conditions

Tina Rathjen

Agriculture and Food

Introduction

My name is Tina Rathjen. I am a Research Project Officer and I am presently working on a project within the GRDC’s National Phenology Initiative. I have been working as a molecular biologist for over 25 years working on both animal and plant systems. The last 10 years I have worked at CSIRO and have worked on several projects investigating different aspects of wheat, which involved me primarily carrying out lab, glasshouse and field experiments. Previously data generated has been entered and analysed using Excel and prior to data school I had not done any coding in R. Data School FOCUS has opened my eyes to a whole new world of data analysis and I hope to use this as a starting point to learn new and exciting things. I have absolutely loved Data School!

My Project

I work on a project within the GRDC’s National Phenology Initiative which aims to predict the flowering time of wheat and barley cultivars in different environmental conditions around Australia.

Wheat and barley cultivars have an optimal flowering window. Crops that flower too early can have reduced yield due to insufficient biomass accummulation and exposure to cold or frost events. Crops that flower too late risk being exposed to water stress or heat events. Growers require accurate information to select the correct cultivar and sowing date for their conditions. For new cultivars it many take several years to conduct field trials across many regions to acccumulate sufficient data to predict flowering time. Currently cultivars can be released prior to trials being carried out and therefore growers may not have the knowledge of which cultivar to plant and what is the correct time of sowing for their conditions.

It is known that major environmental factors influencing flowering time include thermal time, photoperiod and vernalisation. APSIM models have been developed that use parameters based on these factors to model cultivar flowering times. It is the aim of this project to improve & modify the existing APSIM models of wheat and barley. This project involved four controlled temperature experiments being carried out on 54 Australian wheat cultivars and 15 Wheat NILs (Near Isogenic Lines). Data generated from this study together with genomic data will be used to identify molecular markers important in predicting flowering time. The aim is to in the future develop improved APSIM models based on parameterisation with molecular markers, controlled environment and/or genomic data to more accurately predict flowering time.

Preliminary results

The controlled experiments were carried out under four environmental conditions, SN (short days, no vernalisation), LN (long days, no vernalisation, SV (short days plus vernalisation) and LV (long days plus vernalisation) to determine the influence of photoperiod and vernalisation on flowering times and linked traits. Traits measured include emergence date, flowering date, heading date, final leaf number and spikelet number. In addition haun stage, a measure of developmental growth stage based on leaf emergence, was recorded every third day from emergence to flowering. I have used the R package in tidyverse to clean and tidy the data. I have also converted dates to accumulated thermal time (degree days). Table 1 shows a representation of the trait data including flowering time, heading time, spikelet number and final leaf number. Table 2 shows a representation of the haun stage data.

Tables

(#tab:all_data_wide)Controlled Environment Data
Environment Genotype Rep Thermal time to flowering Thermal time to heading Final leaf number Spikelet number
SN EMU_ROCK 1 1186.753 1186.753 8 15
SN CSIROW029 1 1186.753 1186.753 7 17
SN EGA_WILLS 1 2557.172 2472.354 17 23
SN CSIROW102 1 1793.287 1772.176 12 32
SN CSIROW007 1 2663.050 2663.050 16 28
(#tab:haun_data_wide)Haun Stage Data
Environment Genotype Rep Haun stage Thermal time to measurement
SN EMU_ROCK 1 1.5 64.07807
SN EMU_ROCK 1 1.9 149.25758
SN EMU_ROCK 1 2.1 213.08034
SN EMU_ROCK 1 2.7 298.16918
SN EMU_ROCK 1 3.1 362.63703

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Yet another gapminder plot

Figure 1: Yet another gapminder plot

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My Digital Toolbox

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Favourite tool (optional)

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My time went …

My time went on understanding the data and tidying it so I could attempt to work out what it meant. I also spent a large amount of time working out scripts to calculate the parameters. I especially spent time tring to learn about functions and using linear models to determine the parameter BasePhyllochron, I needed a lot of help to get the scripts to work. (Thanks Aswin)

Next steps

I am planning to learn how to use Tassel to carry out GWAS (Genome-wide Association Studies) to identify the molecular markers linked to the parameters I have calculated. I would love to learn more and find more uses for R, this is only the start of my journey.

My Data School Experience

This summary is mostly about your project. However we would also like to hear about other parts of your Data School experience. What aspects of the program did you really enjoy? Have you tried applying the skills you have learned in your daily work? Have you been able to transfer this knowledge to your team members? Any descriptions of the personal impact the program has had are welcome here as well!